Drive Uplift with Clickstream and
Sales Metrics

Optimize Solr Search Results with Machine Learning,
Shopper Behavior and Your Analytics

Is Your Search Solution Too Rigid?

Online retailers are looking for an efficient way to ensure that shoppers receive the most relevant search results from Solr. They need a solution that enables machine learning to automatically optimize Solr search results but also allow for customization to meet business objectives.

Merchants are also looking to drive uplift beyond popularity from clickstream by leveraging valuable analytics and sales metrics on-hand. A tailored approach is necessary but is often time consuming and costly for many online retailers.

Get the Best of Both Worlds

High-quality Solr search results are achieved with influence from shopper engagement behavior along with metrics such as revenue, conversion, units sold, newness, inventory, rating, and sales rank.

FindTuner lets you immediately visualize and take advantage of any data to dynamically rank results, positively influence conversion, and create the perfect mix of products.

FindTuner Provides Consistently Great Search Results Without Manual Effort


AutoTune delivers the best results with no manual effort by learning from shopper’s behavior, purchase history and buying patterns. Using machine learning models, AutoTune drives the most relevant results, provides better shopping experiences, and responds quickly to trends.

AutoTune Algorithms

AutoTune Algorithms enable merchandisers to optimize Solr search results and dynamically rank products with an easy to use interface that makes tuning and testing algorithms simple.

A/B Testing

Easily pass data to FindTuner to execute multivariate tests and find the best strategy for your shoppers.


What Customers Are Saying

Maximize Conversion Rates

Promote more products, categories, and featured items with confidence.